{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 扔骰子"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 本文以扔骰子为例，通过模拟验证了多次独立随机试验后，骰子点数的总和服从正态分布。\n",
    "### 一枚正常骰子掷一万次，并将每次投掷的结果相加得到总和。将上述实验进行多次重复，从理论上我们可证明，总和的均值为35000，标准差为$100·\\sqrt{\\frac{35}{12}} \\approx 170.78$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.mlab as mlab\n",
    "import datetime\n",
    "from collections import defaultdict\n",
    "import scipy.stats as stats\n",
    "import matplotlib.pyplot as plt\n",
    "sns.set_style('darkgrid')\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# N为总模拟次数\n",
    "N = 10000\n",
    "# Nround为每次模拟掷骰子的次数\n",
    "Nround = 10000\n",
    "# 通过随机函数模拟掷骰子的过程,result_list保存每次模拟点数总和\n",
    "result_list = []\n",
    "for i in range(N):\n",
    "    # count保存每次模拟点数总和\n",
    "    count = 0\n",
    "    # 生成Nround个1~6个随机数,代表骰子点数\n",
    "    # Tips: 这里推荐使用向量化操作，可显著提高程序运行速度\n",
    "    count += np.random.randint(1, 7, size=Nround).sum()\n",
    "    result_list.append(count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "每次模拟点数总和的平均数 : 35001.23\n",
      "每次模拟点数总和的中位数 : 35000.00\n",
      "每次模拟点数总和的标准差 : 170.00\n",
      "每次模拟点数总和的偏度   : 0.05\n",
      "每次模拟点数总和的峰度   : -0.01\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# 计算总和的统计量\n",
    "avgRet = np.mean(result_list)\n",
    "medianRet = np.median(result_list)\n",
    "stdRet = np.std(result_list)\n",
    "skewRet = stats.skew(result_list)\n",
    "kurtRet = stats.kurtosis(result_list)\n",
    "print(\n",
    "\"\"\"\n",
    "每次模拟点数总和的平均数 : %.2f\n",
    "每次模拟点数总和的中位数 : %.2f\n",
    "每次模拟点数总和的标准差 : %.2f\n",
    "每次模拟点数总和的偏度   : %.2f\n",
    "每次模拟点数总和的峰度   : %.2f\n",
    "\"\"\" %(avgRet, medianRet, stdRet, skewRet, kurtRet)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "超过1倍standard deviation的次数: 3130, 占比为: 0.313\n",
      "超过2倍standard deviation的次数: 469, 占比为: 0.047\n",
      "超过3倍standard deviation的次数: 31, 占比为: 0.003\n"
     ]
    }
   ],
   "source": [
    "# 超过1倍standard deviation的次数\n",
    "count_1 = np.sum(np.array(result_list) > avgRet + stdRet * 1.0) + np.sum(np.array(result_list) < avgRet - stdRet * 1.0)\n",
    "ratio_1 = 1.0 * count_1/N\n",
    "\n",
    "# 超过2倍standard deviation的次数\n",
    "count_2 = np.sum(np.array(result_list) > avgRet + stdRet * 2.0) + np.sum(np.array(result_list) < avgRet - stdRet * 2.0)\n",
    "ratio_2 = 1.0 * count_2/N\n",
    "\n",
    "# 超过3倍standard deviation的次数\n",
    "count_3 = np.sum(np.array(result_list) > avgRet + stdRet * 3.0) + np.sum(np.array(result_list) < avgRet - stdRet * 3.0)\n",
    "ratio_3 = 1.0 * count_3/N\n",
    "\n",
    "print('超过1倍standard deviation的次数: %d, 占比为: %.3f' %(count_1, ratio_1))\n",
    "print('超过2倍standard deviation的次数: %d, 占比为: %.3f' %(count_2, ratio_2))\n",
    "print('超过3倍standard deviation的次数: %d, 占比为: %.3f' %(count_3, ratio_3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0xa0fd080>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x98399b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 画日对数收益率分布直方图\n",
    "fig = plt.figure(figsize=(18, 9))\n",
    "v = result_list\n",
    "x = np.linspace(avgRet - 3*stdRet, avgRet + 3*stdRet, 100)\n",
    "y = mlab.normpdf(x, avgRet, stdRet)\n",
    "kde = stats.gaussian_kde(v)\n",
    "\n",
    "# plot the histogram\n",
    "plt.subplot(121)\n",
    "plt.hist(v, 50, weights = np.ones(len(v))/len(v), alpha = 0.4)\n",
    "plt.axvline(x = avgRet, color = 'red', linestyle = '--', linewidth = 0.8, label = 'Mean Count')\n",
    "plt.axvline(x = avgRet - 2 * stdRet, color = 'blue', linestyle = '--', linewidth = 0.8, label = '-2 Standard Deviation')\n",
    "plt.axvline(x = avgRet + 2 * stdRet, color = 'blue', linestyle = '--', linewidth = 0.8, label = '2 Standard Deviation')\n",
    "plt.ylabel('Percentage', fontsize = 10)\n",
    "plt.legend(fontsize = 12)\n",
    "\n",
    "# plot the kde and normal fit\n",
    "plt.subplot(122)\n",
    "plt.plot(x, kde(x), label = 'Kernel Density Estimation')\n",
    "plt.plot(x, y, color = 'black', linewidth=1, label = 'Normal Fit')\n",
    "plt.ylabel('Probability', fontsize = 10)\n",
    "plt.axvline(x = avgRet, color = 'red', linestyle = '--', linewidth = 0.8, label = 'Mean Count')\n",
    "plt.legend(fontsize = 12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
